We will experiment and evaluate our regularisers in two settings, a synthetic and a real world one. We will compare
the analytical and the stochastic regulariser, which we will denote by AN and ST respectively, against popular regularisers
used with neural networks, namely \(\ell _2\)  and Dropout [1]}, over different network
architectures. In the real world datasets we also give the results of the Word Mover's Distance,
WMD, [2]} which makes direct use of the side-information to compute document distances.
Obviously our regularisers and WMD have an advantage
over \(\ell _2\)  and dropout since it exploit side-information which \(\ell _2\)  and dropout do not.
